Crystal-liquid Fugacity Ratio as a Surrogate Parameter for Intestinal Permeability
Bibliographic record
Abstract
BACKGROUND: We assessed the feasibility of using crystal-liquid fugacity ratio (CLFR) as an alternative parameter for intestinal permeability in the biopharmaceutical classification (BCS) of passively absorbed drugs. METHODS: Dose number, fraction of dose absorbed, intestinal permeability, and intrinsic dissolution rate were used as the input parameters. CLFR was determined using thermodynamic parameters i.e., melting point, molar fusion enthalpy, and entropy of drug molecules obtained using differential scanning calorimetry. RESULTS: The CLFR values were in the range of 0.06-41.76 mole percent. There was a close relationship between CLFR and in vivo intestinal permeability (r > 0.8). CLFR values of greater than 2 mole percent corresponded to complete intestinal absorption. Applying CLFR versus dose number or intrinsic dissolution rate, more than 92% of tested drugs were correctly classified with respect to the reported classification system on the basis of human intestinal permeability and solubility. CONCLUSION: This investigation revealed that the CLFR might be an appropriate parameter for quantitative biopharmaceutical classification. This could be attributed to the fact that CLFR could be a measure of solubility of compounds in lipid bilayer which was found in this study to be directly proportional to the intestinal permeability of compounds. This classification enables researchers to define characteristics for intestinal absorption of all four BCS drug classes using suitable cutoff points for both intrinsic dissolution rate and crystal-liquid fugacity ratio. Therefore, it may be used as a surrogate for permeability studies. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".